A knowledge-based scheduling system for Emergency Departments

A knowledge-based reactive scheduling system is proposed to answer the requirements of Emergency Departments (EDs). The algorithm includes detailed patient priority, arrival time, flow time and doctor load. The main aim is to determine the patients who have higher priorities initially, and then minimize their waiting times. To achieve this aim, physicians and the other related workers can use an interactive system. In this study, we evaluated the existing system by comparing the proposed system. Also, reactive scheduling cases were evaluated for some items such as decreasing the number of doctors, changing durations and entering of an urgent patient to the system. All experiments were performed with proposed algorithm and right shift rescheduling approach.     

A knowledge-based system for reactive scheduling decision-making in FMS

This paper describes research into the development of an intelligent simulation environment. The environment was used to analyze reactive scheduling scenarios in a specific flexible manufacturing systems (FMS) configuration. Using data from a real FMS, simulation models were created to study the reactive scheduling problem and this work led to the concept of capturing instantaneous FMS status data as snapshot data for analysis. Various intelligent systems were developed and tested to asses their decision-making capabilities. The concepts of History Logging and expert system learning is proposed and these ideas are implemented into the environment to provide decision-making and control across a FMS schedule lifetime. This research proposes an approach for the analysis of reactive scheduling in an FMS. The approach and system that was subsequently developed was based on the principle of automated intelligent decision-making via knowledge elicitation from FMS status data, together with knowledge base augmentation to facilitate a learning ability based on past experiences.     

A self-adaptive agent-based fuzzy-neural scheduling system for a wafer fabrication factory

A self-adaptive agent-based fuzzy-neural system is constructed in this study to enhance the performance of scheduling jobs in a wafer fabrication factory. The system integrates dispatching, performance evaluation and reporting, and scheduling policy optimization. Unlike in the past studies a single pre-determined scheduling algorithm is used for all agents, in this study every agent develops and modifies its own scheduling algorithm to adapt it to the local conditions. To stabilize the performance of the self-adaptive agent-based fuzzy-neural scheduling system, some treatments have also been taken. To evaluate the effectiveness of the proposed methodology and to make comparison with some existing approaches, production simulation is also applied in this study to generate some test data. According to experimental results, the self-adaptive agent-based fuzzy-neural system did improve the performance of scheduling jobs in the simulated wafer fabrication factory, especially with respect to the average cycle time and cycle time standard deviation.     

Intelligent scheduling approaches for a wafer fabrication factory

The production system of a wafer fabrication factory is a very complicated process. Job scheduling in a wafer fabrication factory is a very difficult task. To solve this problem, two intelligent scheduling rules are proposed in this study. The intelligent scheduling rules are modified from the well-known fluctuation smoothing rules with some innovative treatments. To evaluate the effectiveness of the proposed methodology, production simulation was also applied in this study. According to experimental results, the proposed methodology outperformed some existing approaches by reducing the average cycle time and cycle time standard deviation, the most important objectives of job scheduling in a wafer fabrication factory.     

An object-oriented architecture for knowledge-based production scheduling systems

This paper describes an object-oriented architecture to support decision making in production scheduling environments. An object-oriented world view is used to integrate concepts from discrete event simulation, conventional scheduling logic and artificial intelligence to produce capacity-feasible schedules. The architecture was implemented as a collection of loosely coupled reusable software objects by extending the functionality of software objects from BLOCS/M (Berkeley Library of Objects for Control and Simulation of Manufacturing). Our experience with an industrial prototype is presented.     

An investigation into knowledge-based yard crane scheduling for container terminals

To resolve the problems of operational efficiency, energy consumption and operational cost of an entire container terminal, the yard crane scheduling secures a crucial position during terminal operational process. Accordingly, it is imperative to develop an efficient yard crane scheduling strategy. In this study, the knowledge acquisition was initially conducted. Subsequently, a knowledge sorting process, including the taxonomic tree generation and organization of acquired knowledge, was completed. Afterwards, the rules were extracted for the purpose of yard crane scheduling. Furthermore, a mechanism was deployed for knowledge reasoning. Consequently, a knowledge-based system was established with regard to yard crane scheduling. To this end, a case study was used to illustrate the proposed knowledge-based system.     

Student modelling by a knowledge-based system

ELECTRE is a project to build an intelligent tutoring system for learning basic electricity. This paper describes a student model based on the student's cognitive processes. This model includes, for each student, his/her domain knowledge and the specific heuristics. Moreover, it uses meta-knowledge of problem solving. This model is simulated by a knowledge-based system that controls the solving processes by meta-rules. A case study is presented.     

KBMS: a knowledge-based system for modeling software systemspecifications

Recent research results in software engineering have shown that the process of formalizing and automating software specification activity is moving towards knowledge-based specification systems. A modeling approach and a knowledge-based system that assist in the requirement acquisition, development, and maintenance of such software system specifications are presented     

A knowledge-based system for disk-drive diagnostics

A frame-based, backward-chaining knowledge-based system was developed to help the diagnostic process in the manufacture of computer high-end disk drives. The nature of the diagnostic and testing process is presented and the methodology used to develop the knowledge-based system is explained. The development and consultation environments are introduced and the final system is described. The improvements and results obtained with the utilization of this knowledge-based system in a factory environment are also included.     

A knowledge-based system for visualization design

Vista is a knowledge-based system that helps scientists design visualization techniques. It generates a technique for a given data set and lets users modify the design interactively using a compositional design methodology. To ensure the effectiveness of its designs, Vista uses many rules, mostly heuristic in nature, that were acquired through literature surveys and discussions with visualization experts. In general, Vista's design was based on research in graphical perception. It extends the design methodology of Automatic Presentation Tool (APT) (J.D. Mckinlay, 1986), a presentation tool for 2D graphics, to three dimensions. While Vista's primary function is to automatically generate an effective visualization technique design for a given data set, it also allows users to interactively modify this design and renders the resulting image using a variety of rendering algorithms     

A knowledge-based system for sensor selection

This paper presents a Knowledge-based system (KBS) developed to allow users, who may not be knowledgeable about sensors, to select sensors suitable for their specific needs. The KBS runs on a micro-computer. The selection criteria are user specified and are based on the desired measurement parameters. The system output includes all of the operational and dimensional parameters of the recommended sensor, price, and vendor information.     

An optimized tailored nonlinear fluctuation smoothing rule for scheduling a semiconductor manufacturing factory

An optimized tailored nonlinear fluctuation smoothing rule is proposed in this study to improve the performance of scheduling jobs in a semiconductor manufacturing factory. The tailored nonlinear fluctuation smoothing rule is modified from the well-known fluctuation smoothing rules with some innovative treatments. At first, the fuzzy c-means and fuzzy back propagation network approach is applied to estimate the remaining cycle time of every job. Then the nonlinear fluctuation smoothing rules are constructed and tailored to the semiconductor manufacturing factory to be scheduled. Finally, the back propagation network optimization approach is proposed to optimize the rule. To evaluate the effectiveness of the proposed methodology, production simulation is also applied in this study to generate some test data. According to experimental results, the proposed methodology outperformed nine existing approaches in reducing the cycle time averages and standard deviations.     

TUNEX: a knowledge-based system for performance tuning of the UNIXoperating system

TUNEX, an expert system developed for performance tuning of the UNIX operating system, is described. TUNEX was developed on UNIX system V. It uses the properties, commands and utilities of this version. The tuning activities it is concerned with include: (1) adjusting operating system tunable parameters, such as number of disk buffers; (2) running maintenance routines, i.e. reorganizing file systems; (3) developing operation rules, such as off-peak hour runs of backups; and (4) modifying hardware, buying an additional disk drive. The structure of TUNEX is presented and performance analysis modules which provide quantitative information to this tool are briefly described. The overhead in the resource usage introduced by the performance monitoring and tuning tool itself is discussed; the author points to the areas in which additional resources are required by TUNEX     

Designing a knowledge-based system for benchmarking: A DEA approach

First developed by Xerox in 1979, benchmarking provides measurement and comparison to improve processes and achieve higher performance. Benchmarking has proven a powerful tool for total quality management and process improvement. Successful benchmarking implementation is based on an effective benchmarking tool. To effectively implement benchmarking processes, this work proposes an integrated framework for the benchmarking tool and knowledge-based system using the data envelopment analysis (DEA) method, and then develops an intellectual benchmarking knowledge-based system (BKBS) for benchmarking, performance evaluation and process improvement. Accordingly, this work illustrates how the benchmarking knowledge-based system (BKBS) is implemented in a medical center. This system can help determine the particular benchmarking partners to evaluate the relative efficiency and fill the gaps between the benchmarking partners in the healthcare industry. Finally, the intellectual benchmarking knowledge-based system offers a very fast way to implement the benchmarking processes.     

Design and validation of a knowledge-based system for screeningproduct innovations

The authors' research goal is to develop and validate an expert system that screens innovations prior to commercialization. This is an important research issue because business corporations are highly dependent on innovations for their growth and profitability, yet most corporations suffer from a high rate of new product failure. Few of the existing decision support systems have alleviated this problem, partly because of their inability to deal with nonmathematical (logical) relationships. An expert system for new product planning could save organizations tremendous amounts of resources (such as dollars, time and scientific talent) spent on product failures. The design of the proposed knowledge-based system is built upon the authors' earlier work in this area. The authors have addressed several critical research issues in the development of such a system: choice of the appropriate sources of knowledge, resolution of conflict among human experts chosen for knowledge acquisition, use of knowledge programming techniques that can accommodate uncertainty, and multiple methods of system validation. The research makes several contributions to marketing theory and practice. Most notably, the development of such systems contributes to effective product planning in organizations and enhances resource efficiency. Further, it generates guidelines for capturing and using expertise in highly unstructured decision-making situations such as product management     

Developing an explanation component for a knowledge-based system: Discussion

The articles in this special issue cover a wide range, and represent many different conceptions of “explanation” and what explanation research should be about. In this short discussion article, I will address some of the implications of this work for those trying to develop a practical explanation system. In particular I will focus on how explanation needs are determined, how detailed explanation content is chosen, and how the explanations provided relate to the problem solving activities of the system.     

Development of a knowledge-based self-assessment system for measuring organisational performance

Effective performance measurement is an important task in the discipline of engineering management. With the support of City University of Hong Kong, a research project was initiated to develop a knowledge-based expert self-assessment (KES) training toolkit on measuring and assessing organisational performance based on the evaluation criteria of a renowned Business Excellence Model—the Malcolm Baldrige National Quality Award (MBNQA). This paper explains the development of and elaborates the system framework, requirements, design and validation of the toolkit. The project results shows that the toolkit could facilitate the teaching of students of engineering management courses by providing a stimulating learning environment and practical experiences in measuring and assessing enterprise performance. Incorporating the KES model and toolkit into the engineering management curriculum can provide students and industrial users with hands-on experience and insights of organisational PM.